用未来预测生成代码任务数据，避免历史重复，提升真实感。
SWE-Future: Forecast-Conditioned Data Synthesis for Future-Oriented Software Engineering Agents
- 基于历史代码库数据预测未来任务类型
- 80仓库验证中预测准确率达58.1%
- 适合构建不依赖历史回放的下一代代码代理测试集
真实代码代理评测常复用公开GitHub问题和合并请求，易与模型预训练、微调或合成数据产生重叠。完全合成的任务虽避免直接历史回放，但可能偏离真实仓库需求。本文提出SWE-Future，一种基于未来预测的代码任务合成方法。给定时间T₀的预测快照，仅使用T₀前的仓库证据来预测未来的功能实现、增强、修复与重构任务类别。我们首先通过回溯验证：固定预测后，仅用后续合并请求衡量预测任务类别与实际工作的一致性。在80个仓库的研究中，预测在主要语义匹配指标下达到58.1%的未来工作相关性。随后，将经验证的预测类别作为条件信号，从任务生成快照中合成跨61个仓库的200个任务数据集，而非复用用于验证的后期合并请求。SWE-Future表明，代码库演化预测可指导真实且面向未来的代码任务生成，同时降低对历史合并请求回放的依赖。
原文摘要 · Abstract (English)
Realistic coding-agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generation, or benchmark-driven model selection. Fully synthetic tasks avoid direct historical replay, but can drift away from real repository needs. We propose SWE-Future, a forecast-conditioned data synthesis method for future-oriented coding tasks. Given a forecast snapshot at time $T_0$, the method uses only pre-$T_0$ repository evidence to forecast future feature implementation/enhancement, bugfix, and refactor task families. We first validate this forecasting step retrospectively: after forecasts are fixed, later pull requests are used only to measure whether the predicted task families match future repository work. In an 80-repository study, the forecaster achieves 58.1\% future-work relevance under the main semantic matching metric. We then use validated forecast families as conditioning signals to synthesize a 200-task coding-agent dataset across 61 repositories from a task-generation snapshot, rather than replaying the later pull requests used for validation. SWE-Future shows that repository-evolution forecasts can guide realistic, future-oriented coding-task synthesis while reducing direct dependence on historical pull-request replay.
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